How Google detects automatically generated content

How Google detects automatically generated content is usually the wrong question: Google is not looking for "text written by a machine", but content with no value for the reader, and the tool used to write it does not matter. The official position, since 2023: AI-generated content does not in itself violate the policies; content mass-produced to manipulate rankings does, whether it is written by a program or a team of people. This guide explains what Google's systems detect, what they cannot detect, what changed in 2024 with the scaled content abuse policy, and how to publish AI-assisted content that survives any update.

We write from direct experience: on our website, some articles are written with the help of language models, with human review, sources and our own data, and our publishing principles are public.

How Google detects automatically generated content

What the policy says

The spam policies define "automatically generated content" as spam only when it is produced without value, with the aim of manipulating rankings: meaningless text stuffed with keywords, unreviewed machine translations, text assembled from sources without adding value, and pages generated from templates. Since March 2024, the category has been called scaled content abuse and explicitly covers any method, human or automated, that produces many pages without value. There is no "AI prohibited" rule; there is a "no value prohibited" rule.

What Google actually does

Ranking systems assess quality and usefulness: the originality of the information, the experience demonstrated, accuracy, user satisfaction after a click, and the website's trust signals. Properly generated text, with verified facts and added experience, has the same signals as text written by hand; generic text with nothing new has the signals of generic text, regardless of its author. What Google "detects" in practice is patterns of poor content, which happen to be common in unsupervised generation.

Signals that reveal content without value

• Lack of information gain: the page says what the top ten results already say, in different words. The concept and how to measure it are covered in the article on the information gain score.
• Generalisations instead of facts: "it is essential", "plays a crucial role", without figures, dates, examples or names.
• Identical structure across hundreds of pages: the same subheadings, the same length, the same transitional phrases.
• Factual errors and invented references: studies that do not exist, figures without sources, tools with imaginary features.
• Lack of experience: nothing that cannot be found by reading other websites; no "we measured", "at client X", or "in our case".
• Post-click signals: visitors quickly return to the results because they have not found the answer.
• Sudden volume: 500 articles published in a month on a website that used to publish two, on subjects unrelated to its activity.
• An absent or invented author, with no trace outside the website.

None of these is an "AI detector"; they are all detectors of poor content. An article written by a tired human, drawn from three other articles, has the same signals.

What Google cannot detect (and neither can anyone else, with certainty)

Publicly available AI text detectors have high error rates in both directions and are unusable as evidence; Google has not announced that it uses them for ranking. Statistical watermarks (Google SynthID for Gemini, similar systems from other providers) can mark text generated by a particular model, but their detection is available only to the provider or its partners, not to a search engine for any text on the web, and editing the text weakens it. In short: rely neither on "Google cannot know" nor on "Google knows for certain"; rely on the value of the content, the only thing assessed with certainty.

What changed in 2024 and 2025

1. March 2024: the core update integrated the helpful content system and, according to Google, reduced low-quality content in the results by approximately 45%; websites with a high volume of generic articles lost the most.
2. March 2024: the new policies (scaled content abuse, site reputation abuse, expired domain abuse), with manual actions applied to entire websites in the first weeks.
3. 2024-2025: the expansion of generated answers (AI Overviews, AI Mode), which cite sources with authority and experience, rather than summaries of other summaries; a website that produces only summaries has nothing to offer a system that does its own summarising.

Cases from spam updates, including the March 2026 update, are analysed in the article on the March 2026 spam update, and diagnosing a decline is covered in the article on recovering from a penalty.

How to use AI properly for content

Where it helps

• Research and structure: grouping by intent, planning subheadings, and identifying questions to cover, following the method in the article on keyword research with Claude and ChatGPT.
• A first draft based on a brief containing your own data, followed by human editing.
• Rewriting for clarity, sentence case, consistency, and translations for review.
• Checks: sentences without facts, repetition, filler words, and missing sources.

Where it does not help

• Facts: the model invents figures, studies and quotations; every verifiable claim must be checked.
• Experience: the model has not worked for your client; you write the "from practice" sections yourself.
• Volume: 100 articles a month without an editor is exactly the pattern described in the policy.
• Subjects in which you have no expertise: an accounting website that publishes about medicine loses both trust and rankings.

The process we follow

1. A brief containing the subject, intent, our own data, permitted sources, what competitors already say, and what is missing.
2. Generation based on the brief, with style instructions and a prohibition on inventing figures.
3. Automated checks: length, structure, sources from the permitted list, prohibited words, form of address, and existing internal links. On our website, a quality gate rejects any article that fails.
4. Human editing: verified facts, added experience, figures from projects, and cuts.
5. A real author, byline, cited sources, and date.
6. Measurement after publication: impressions, rankings, click-through rate, and returning visits; articles that add nothing in three months are rewritten or removed.

The brief for writers (human or otherwise) is described in the article on the SEO brief for writers, and the steps for actually writing with a model are in the guide to writing an article with Claude.

Example: two versions of the same article

The subject: how long it takes to see SEO results. The version generated without a brief: six paragraphs saying that "it depends on many factors", that "SEO is a long-term process", and that "patience is essential". No figures, no sources, nothing that does not already exist in 200 other articles. The version based on a brief, with our own data: the timeframes seen across 30 clients (initial improvements for commercial phrases between the third and sixth month, stable rankings at 9-12 months), the difference between new websites and websites with a history, two factors that shortened the timeframe for our clients (internal links and consolidating duplicates), a section on what cannot be accelerated, and a reference to Google's document on core updates. Both are "generated"; only the second is content. The difference is made by the brief, not the detector.

The real risk: the website's reputation

Quality signals apply at website level, not just page level: a website with 300 generic articles and 20 good ones is assessed as a generic website, and those 20 suffer. That is why, before publishing new AI-assisted content, you should clean up what already exists: articles without value should be expanded with real data or removed. On our blog, this clean-up meant expanding 54 articles and redirecting 13, before publishing any new article.

Transparency: should you say so?

Google does not require a "written with AI" label, but it does require an identified, accountable author. Our recommendation: a publishing principles page explaining how you work (tools, verification, who is responsible), without a label on every article; readers and systems assess the result, and responsibility lies with the named author. Our policy is public on the publishing principles page.

What Google checks in a manual review

Human evaluators (quality raters) have sections in their public guidelines dedicated to content generated with minimal effort, including with AI: pages that "seem" written for search engines, without expertise or a clear purpose for the visitor, receive the lowest quality rating. Their ratings do not directly change rankings, but they train the systems; what they consider low-quality content becomes, over time, what the algorithm also considers low-quality content. Reading the chapter on quality in the evaluators' guidelines remains the best test for any article, generated or otherwise.

Frequently asked questions

1. If I rewrite an AI article "so it does not show", is it safer? No; rewriting does not add value. Add facts and experience, not synonyms.
2. Can I publish 50 generated articles a month? You can; if each has information gain and verification, it is content; otherwise, it is scaled content abuse, regardless of the tool.
3. Does Google penalise websites that mention using AI? There is no signal indicating this; quality matters.
4. What should I do with old generic articles? Expand them with your own data or consolidate them; we have done both on our blog and recommend them in the article on strategies for increasing organic traffic.

Sources

• Google Search Central, spam policies: scaled content abuse and other violations.
• Google Search Central, helpful, reliable, people-first content: self-assessment questions, including those about generated content.

Source references

• Google Search Central, politicile privind spamul în Căutare
• Google Search Central, conținut util, de încredere, scris pentru oameni